Article
Diet and Fitness Recommender Using MCP and LLM
This paper presents an intelligent Diet and Fitness Recommender System that integrates Machine Learning (ML), mathematical optimization, and conversational artificial intelligence through the Model Context Protocol (MCP) and Large Language Models (LLMs). The system estimates calories burned during physical activity using a trained Artificial Neural Network (ANN) regression model, taking into account physiological parameters such as age, weight, height, heart rate, body temperature, gender, and workout duration. Predicted caloric expenditure is then used to drive a Linear Programming (LP) optimization module that generates personalized diet plans maximizing protein intake under caloric and macronutrient constraints. An LLM is integrated via MCP as an interactive conversational agent that coordinates backend analytical tools, interprets user queries, and presents recommendations in natural language. The system achieves an ANN Mean Absolute Error (MAE) of 28 kcal, an R² score of 0.93, a Nutrition Constraint Satisfaction Rate (CSR) of 97%, and a System Usability Scale (SUS) score of 82 out of 100 across a 25-participant user study. Experimental outcomes confirm that combining predictive analytics, LP optimization, and conversational AI delivers a more personalized, accurate, and engaging health recommendation experience than conventional approaches
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